In ideal circumstances, business is carried out between vendors and customers in a manner that is fair and consistent with the law. In practice, however, fair business practices can be subject to fraud, or deliberate deception by one or more individuals or parties for personal gain and/or to cause harm to others persons or parties. A result is an illegal and unfair advantage for a party committing fraud.
Given the subversive nature of fraud, such activities can be well hidden and difficult to identify and trace to the responsible parties. Routing out the cause, including identifying entities indicative of fraud, can be a difficult if not sometimes an insurmountable task. For example, an accredited 2014 survey conducted across 100+ countries, by the Association of Certified Fraud Examiners (ACFE), shows that on average 5% of a company's revenue is lost because of unchecked fraud every year. The reason for such heavy losses according to them is that it takes around 18 months for a fraud to be caught and audits catch only 3% of the actual fraud. A large portion of risky activity is caught through whistle blowers.
In the modern era, a phenomenal amount of digital data is involved in nearly every type of business. Modern developments in both software and hardware have allowed for data analysis techniques to be developed and directed to detecting and identifying fraud and its perpetrators. In the art of fraud detection and risk analysis, analytical systems are developed and relied upon to analyze data and make predictions as to the presence of risk/fraud. Despite considerable advances in fraud detection, the ways in which parties can commit fraud have also advanced and become more elusive. There is a persisting need for novel techniques and systems for the detection and identification of fraud and the conspirators responsible, such that these techniques have a low false positive rate.